code: def _train_step(
      model: _FlaxPenguinModel,
      tx: optax.GradientTransformation,
      params: _Params,
      opt_state: optax.OptState,
      inputs: _InputBatch,
      labels: _LabelBatch,
  ):
      """Train for a single step, given a batch of inputs and labels."""
  
      def loss_fn(params):
          logits = model.apply({"params": params}, inputs)
          loss = _categorical_cross_entropy_loss(logits, labels)
          return loss, logits
  
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      (_, logits), grads = grad_fn(params)
      updates, opt_state = tx.update(grads, opt_state)
      params = optax.apply_updates(params, updates)
      metrics = _compute_metrics(logits, labels)
      return params, opt_state, metrics
  
imports: import functools
  from typing import Callable, Dict, List
  import absl
  from flax import linen
  from flax.metrics import tensorboard
  import jax
  from jax import numpy
  from jax.experimental import jax2tf
  import numpy
  import optax
  import tensorflow
  import tensorflow_transform
  from tfx import v1
  from tfx.examples.penguin import penguin_utils_base
label_desc: Use image node_id 4 for calling a global function with example usage: _train_step(model, tx, params, opt_state, inputs, labels) and returns: params,  opt_state,  metrics, loss,  logits